The availability of datasets annotated with verified events by the public is a necessary prerequisite for unleashing the potential of multimodal deep learning for news event detection. Publicly available datasets are either incompletely annotated due to expensive cost, or ignore the verifiability of event labels, which are susceptible to bias and errors introduced by a limited number of annotators. In this paper, we provide a YouTube dataset labelled by real-world news events that can be verified by Wikipedia-like crowd sourcing platforms, with the target of advancing temporal event detection. The events in our dataset cover a wide range of event topics including public security, natural disasters, elections, sports, and entertainment events, etc. In the dataset, each sample is labelled with real-world event that is verifiable by the public. We extensively evaluate the performance of 13 state-of-the-art algorithms on our dataset in a temporal manner, involving the multiple relationships between training and testing event labels, and provide a thorough analysis of the findings. The dataset is available at https://github.com/zhengyang5/TED .